Gradient descent

Results: 277



#Item
221Adaptive Subgradient Methods  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization∗ John Duchi

Adaptive Subgradient Methods Adaptive Subgradient Methods for Online Learning and Stochastic Optimization∗ John Duchi

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Source URL: www.cs.berkeley.edu

Language: English - Date: 2011-03-18 22:06:19
222Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling arXiv:1005.2012v3 [math.OC] 10 Apr[removed]John C. Duchi1

Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling arXiv:1005.2012v3 [math.OC] 10 Apr[removed]John C. Duchi1

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Source URL: arxiv.org

Language: English - Date: 2011-04-12 10:45:22
223Algorithms for Optimal Decisions Tutorial 3 Answers Exercise 1 Show that the steepest descent direction −

Algorithms for Optimal Decisions Tutorial 3 Answers Exercise 1 Show that the steepest descent direction −

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Source URL: www.doc.ic.ac.uk

Language: English - Date: 2006-11-15 10:45:06
    224Package ‘deepnet’ July 2, 2014 Type Package Title deep learning toolkit in R Version 0.2 Date[removed]

    Package ‘deepnet’ July 2, 2014 Type Package Title deep learning toolkit in R Version 0.2 Date[removed]

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    Source URL: cran.r-project.org

    Language: English - Date: 2014-07-02 11:36:17
    225Algorithms for Optimal Decisions Tutorial 4 Answers Exercise 1 Solve the following Q.P. using the Frank–Wolfe method:

    Algorithms for Optimal Decisions Tutorial 4 Answers Exercise 1 Solve the following Q.P. using the Frank–Wolfe method:

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    Source URL: www.doc.ic.ac.uk

    Language: English - Date: 2006-11-15 10:45:06
    226- (2010) Introduction 1 -  ALGORITHMS FOR OPTIMAL DECISIONS 1. INTRODUCTION: ì Nonlinear decision problems ì Basic concepts and optimality

    - (2010) Introduction 1 - ALGORITHMS FOR OPTIMAL DECISIONS 1. INTRODUCTION: ì Nonlinear decision problems ì Basic concepts and optimality

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    Source URL: www.doc.ic.ac.uk

    Language: English - Date: 2010-09-24 09:40:06
    227Package ‘gettingtothebottom’ August 2, 2014 Title Getting to the Bottom, A Package for Learning Optimization Methods Description Getting to the Bottom is a companion package for the ``Getting to the Bottom'' optimiza

    Package ‘gettingtothebottom’ August 2, 2014 Title Getting to the Bottom, A Package for Learning Optimization Methods Description Getting to the Bottom is a companion package for the ``Getting to the Bottom'' optimiza

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    Source URL: cran.r-project.org

    Language: English - Date: 2014-08-02 17:50:43
    228arXiv:1105.4701v3 [cs.LG] 8 Sep[removed]Online Learning, Stability, and Stochastic Gradient Descent September 9, 2011  Tomaso Poggio, Stephen Voinea, Lorenzo Rosasco

    arXiv:1105.4701v3 [cs.LG] 8 Sep[removed]Online Learning, Stability, and Stochastic Gradient Descent September 9, 2011 Tomaso Poggio, Stephen Voinea, Lorenzo Rosasco

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    Source URL: cbcl.mit.edu

    Language: English - Date: 2011-09-15 10:11:06
    229arXiv:1105.4701v3 [cs.LG] 8 Sep[removed]Online Learning, Stability, and Stochastic Gradient Descent September 9, 2011  Tomaso Poggio, Stephen Voinea, Lorenzo Rosasco

    arXiv:1105.4701v3 [cs.LG] 8 Sep[removed]Online Learning, Stability, and Stochastic Gradient Descent September 9, 2011 Tomaso Poggio, Stephen Voinea, Lorenzo Rosasco

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    Source URL: cbcl.mit.edu

    Language: English - Date: 2011-09-15 10:11:06
    230Gradient Descent with Sparsification: An iterative algorithm for sparse recovery with restricted isometry property Rahul Garg [removed] Rohit Khandekar

    Gradient Descent with Sparsification: An iterative algorithm for sparse recovery with restricted isometry property Rahul Garg [removed] Rohit Khandekar

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    Source URL: www.machinelearning.org

    Language: English - Date: 2009-05-18 12:16:39